ArticleBreast cancer research : BCR2016
Determining breast cancer histological grade from RNA-sequencing data.
Article in Breast cancer research : BCR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 63 citations in OpenAlex.
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- Analyzing the Gaps in Breast Cancer Diagnostics in Poland-A Retrospective Observational Study in the Data Donation Model.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning-based single-sample molecular classifier for cancer grading.Frontiers in oncology · 2025Article
- Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning.BMC cancer · 2024Article
- Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.Breast cancer research : BCR · 2024Article
- Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images.Heliyon · 2024Article
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- Integrated multi-omics profiling of high-grade estrogen receptor-positive, HER2-negative breast cancer.Molecular oncology · 2022Article
- Cell-to-cell interaction analysis of prognostic ligand-receptor pairs in human pancreatic ductal adenocarcinoma.Biochemistry and biophysics reports · 2021Article
- Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group.Biomolecules · 2021Article
- Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer.Cancer research · 2021Article
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- Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer.British journal of cancer · 2021Article
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6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe histologic grade (HG) of breast cancer is an established prognostic factor. The grade is usually reported on a scale ranging from 1 to 3, where grade 3 tumours are the most aggressive. However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making. Patients classified as grade 2 are at risk of both under- and over-treatment.
methodsRNA-sequencing analysis was conducted in a cohort of 275 women diagnosed with invasive breast cancer. Multivariate prediction models were developed to classify tumours into high and low transcriptomic grade (TG) based on gene- and isoform-level expression data from RNA-sequencing. HG2 tumours were reclassified according to the prediction model and a recurrence-free survival analysis was performed by the multivariate Cox proportional hazards regression model to assess to what extent the TG model could be used to stratify patients. The prediction model was validated in N=487 breast cancer cases from the The Cancer Genome Atlas (TCGA) data set. Differentially expressed genes and isoforms associated with HGs were analysed using linear models.
resultsThe classification of grade 1 and grade 3 tumours based on RNA-sequencing data achieved high accuracy (area under the receiver operating characteristic curve = 0.97). The association between recurrence-free survival rate and HGs was confirmed in the study population (hazard ratio of grade 3 versus 1 was 2.62 with 95 % confidence interval = 1.04-6.61). The TG model enabled us to reclassify grade 2 tumours as high TG and low TG gene or isoform grade. The risk of recurrence in the high TG group of grade 2 tumours was higher than in low TG group (hazard ratio = 2.43, 95 % confidence interval = 1.13-5.20). We found 8200 genes and 13,809 isoforms that were differentially expressed between HG1 and HG3 breast cancer tumours.
conclusionsGene- and isoform-level expression data from RNA-sequencing could be utilised to differentiate HG1 and HG3 tumours with high accuracy. We identified a large number of novel genes and isoforms associated with HG. Grade 2 tumours could be reclassified as high and low TG, which has the potential to reduce over- and under-treatment if implemented clinically.
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